A generative vision-language model for holistic pathological assessment using preoperative imaging in hepatocellular carcinoma.

ABSTRACT:

Background

Pathological evaluation of hepatocellular carcinoma (HCC) traditionally relies on surgical resection, posing risks of infection and complications while failing to provide comprehensive pathological insights preoperatively. This study aims to develop HepaPathGPT, which utilises preoperative imaging to deliver detailed pathological interpretations, enabling non-invasive, real-time pathological assessments for patients with HCC.

Methods

A retrospective study of 1091 patients with HCC from 10 independent cohorts was used. SegFormer-b5 segmented tumour regions, and vision-language alignment mapped imaging features to pathology descriptions. We fine-tuned four pretrained frameworks using Low-Rank Adaptation (LoRA) to efficiently translate imaging features into structure

SUBMITTER: Wang L 

PROVIDER: S-EPMC12719682 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature